Identification and Quantification of Aqueous Disinfectants Using an Array of Carbon Nanotube-Based Chemiresistors
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Disinfection of water is essential to prevent the growth of pathogens, but at high levels, it can cause harm to human health. Therefore, accurate monitoring of disinfectant concentrations in water is essential to ensure safe drinking water. The use of multiple disinfectants at different stages in water treatment plants makes it necessary to also identify the type and concentrations of all of the disinfectant species present. Here, we demonstrate an effective approach to identify and quantify multiple disinfectants (using the example of free chlorine and potassium permanganate) in water using single-walled carbon nanotube (SWCNT)-based reagent-free chemiresistive sensing arrays. Facile fabrication of chemiresistive devices makes them a popular choice for the implementation of sensor arrays. Our sensing array consists of functionalized and unfunctionalized (blank) SWCNT sensors to distinguish the disinfectants. The distinct responses from the different sensors at varying concentrations and pH can be fitted to the mathematical model of a Langmuir adsorption isotherm separately for each sensor. Blank and functionalized sensors respond through different mechanisms that result in varying responses that are concentration- and pH-dependent. Chemometric techniques such as principal component analysis (PCA) and partial least-squares-discriminant analysis (PLS-DA) were used to analyze the sensor data. PCA showed an excellent separation of the analytes over five different pHs (5.5, 6.5, 7.5, 8.5, and 9.5). PLS-DA provided excellent separability as well as good predictability with a Q 2 of 94.26% and an R 2 of 95.67% for the five pH regions of the two analytes. This proof-of-concept solid-state chemiresistive sensing array can be developed for specific disinfectants that are commonly used in water treatment plants and can be deployed in water distribution and monitoring facilities. We have demonstrated the applicability of chemiresistive devices in a sensor array format for the first time for aqueous disinfectant monitoring.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".